Data, Analytics, and Insight: What's the Difference?


The world of data can be complex and tricky to navigate.  

What makes things more confusing is when the words ‘data’, ‘analytics’ and ‘insights’ are used interchangeably.  

Although it’s understandable that people often get these terms confused, each of them has an individual meaning and function.  

So what exactly is data? How is data different from analytics? And how do you get valuable insights from all that information?   

The answer can be found in three simple steps: First, you collect your data. Then you use analytics to organise and interpret that data. Finally, your insights are the knowledge and valuable actions you take away.  

Let’s explore each step in more detail:  

What is data?

In its raw form, data is the information that you gather about your customers, products and services. This can be the number of transactions, sales, user demographics, conversions, dates, weights, locations or other important statistics.  

These days, data is generated every time we engage with technology, whether that’s opening an app or making an online purchase. As a result, every organisation collects substantial amounts of data.  

Data is acquired as a collection of facts and figures and is relatively useless on its own. A data set is a collection of information and rarely contains any context. In order to gain any insights from your data it must be processed and analysed first.  

How is data collected? 

So how do organisations go about collecting such vast amounts of data? Data collection happens on multiple levels.  

Companies use IT systems to collect data on their customers, employees, sales, transactions and other aspects of their operation. They may also conduct marketing activities such as surveys, advertising and social media marketing, all of which will generate useful data.  

Big data refers to more complex data sets like customer databases, records, clickstreams, apps, videos and social networks. Big data is so large or complex that it’s difficult to process and analyse. Specialised data software tools are required to handle and analyse this kind of complex data.  

In order for data to be valuable, it’s important for companies to determine the type of data that needs to be collected and how the data is grouped. For example, it may be separated into clusters (or segments) such as age and income, or divided by numerical categories.  

Data must then be stored in a safe location such as a data centre or using cloud technology. Data warehousing is an important factor in data collection that can influence the scalability, consistency and performance of your data.  

What are analytics?  

Analytics is the science of analysing your raw data to give different interpretations of the information. Data analytics can reveal metrics and statistics that would otherwise be lost in a mass of information.  

This information can then be interpreted into valuable data insights to help you improve your operation.  

What tools are used to analyse data?

Data is analysed using a range of tools and is often the job of a data analyst.  

Tools range from running data through a Microsoft spreadsheet to specialist software that transforms, manipulates and interrupts your data like Power BI and Google Analytics.  

Analytics tools compile information and present the results in a user-friendly format such as reports, dashboards, graphs and models.  

What are insights? 

Insights are the knowledge gained from analysing your data and providing conclusions and predictions that can benefit your business. 

Gaining valuable data-driven insights is the final step in making your data work for you. Analytical insights transform your data into meaningful information.  

For example, if you are an e-commerce website running an advertising campaign you may use data insights to optimise your adverts. Data insights may show you that customers are mainly shopping between 5pm and 8pm, so you increase your marketing budget to show more ads during that time.  

You could also reduce your ads at other times of the day when your customers are less active to save on advertising spend.  

Data insights allow you to truly understand your customers and how your business functions. They provide a blueprint for improving your operational functions and create better value for your customers.  

At NashTech, we use advanced analytics tools and machine learning models to gain deeper insights from your data, make predictions and generate recommendations. Learn more about our analytics and AI solutions on our Advanced Analytics Page. 

How do data, analytics and insights work together?  

In order to truly make data work for your business, it’s important to identify your business goals and objectives. Ask yourself, what questions do you need the answer to in order to improve? 

Perhaps you need to fully understand how your customers interact with your business so that you can optimise the sales process and drive transactions. Alternatively, it may be that you need to analyse the productivity of a machine or system so you can find ways to manufacture more products in a shorter time frame.  

Whether you’re a small start-up business, or a large corporation, having a solid data strategy is key to success. Using data collection, analytics and actionable insights can help you reach your goals and business objectives for a more profitable future.  

Navigating the complex world of data with NashTech

Do you need help in realising the untapped potential of your data? Let us work with you to transform your business information into knowledge.  

As a fundamental asset to your organisation, data is key to its success and its profitability. At Nashtech we specialise in providing data management and intelligent analytics solutions that are tailored to your business.  

From data strategy and warehousing systems to data migration, business intelligence services and advanced analytics.  

Discover our range of data, analytics and insights solutions or contact our team to discuss your requirements today: Data, Analytics and AI 

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